Pytorch实现网络部分层的固定不进行回传更新问题及思路详解

class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.conv1 = nn.Conv2d(3,32,3)
        self.conv2 = nn.Conv2d(32,24,3)
        self.prelu = nn.PReLU()
        for m in self.modules():
            if isinstance(m,nn.Conv2d):
                nn.init.xavier_normal_(m.weight.data)
                nn.init.constant_(m.bias.data,0)
            if isinstance(m,nn.Linear):
                m.weight.data.normal_(0.01,0,1)
                m.bias.data.zero_()
    def forward(self, input):
        out = self.conv1(input)
        out = self.conv2(out)
        out = self.prelu(out)
        return out

model = Net()
for name, p in model.named_parameters():
    if name.startswith('conv1'):
        p.requires_grad = False
        
optimizer = torch.optim.Adam(filter(lambda x: x.requires_grad is not False ,model.parameters()),lr= 0.2)

for p in model.parameters():
    print(p.requires_grad)